Configurable calorimeter simulation for AI applications

Anton Charkin-Gorbulin; Kyle Cranmer; Francesco Armando Di Bello; Etienne Dreyer; Sanmay Ganguly; Eilam Gross; Lukas Heinrich; Marumi Kado; Nilotpal Kakati; Patrick Rieck; Lorenzo Santi; Matteo Tusoni

doi:10.1088/2632-2153/acf186

Configurable calorimeter simulation for AI applications

Anton Charkin-Gorbulin
, Kyle Cranmer
, Francesco Armando Di Bello
, Etienne Dreyer
, Sanmay Ganguly
, Eilam Gross
, Lukas Heinrich
, Marumi Kado
, Nilotpal Kakati
, Patrick Rieck
, Lorenzo Santi
, Matteo Tusoni

Department of Particle Physics and Astrophysics

Weizmann Institute of Science

Research output: Contribution to journal › Article › peer-review

Abstract

A configurable calorimeter simulation for AI (CoCoA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia event generator. This open-source project is aimed to support the development of machine learning algorithms in high energy physics that rely on realistic particle shower descriptions, such as reconstruction, fast simulation, and low-level analysis. Specifications such as the granularity and material of its nearly hermetic geometry are user-configurable. The tool is supplemented with simple event processing including topological clustering, jet algorithms, and a nearest-neighbors graph construction. Formatting is also provided to visualise events using the Phoenix event display software.

Original language	English GB
Article number	035042
Number of pages	11
Journal	Machine Learning: Science and Technology
Volume	4
Issue number	3
DOIs	https://doi.org/10.1088/2632-2153/acf186
State	Published - 5 Sep 2023

ASJC Scopus subject areas

Software
Human-Computer Interaction
Artificial Intelligence

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10.1088/2632-2153/acf186License: CC BY

Cite this

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abstract = "A configurable calorimeter simulation for AI (CoCoA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia event generator. This open-source project is aimed to support the development of machine learning algorithms in high energy physics that rely on realistic particle shower descriptions, such as reconstruction, fast simulation, and low-level analysis. Specifications such as the granularity and material of its nearly hermetic geometry are user-configurable. The tool is supplemented with simple event processing including topological clustering, jet algorithms, and a nearest-neighbors graph construction. Formatting is also provided to visualise events using the Phoenix event display software.",

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AU - Charkin-Gorbulin, Anton

AU - Cranmer, Kyle

AU - Di Bello, Francesco Armando

AU - Dreyer, Etienne

AU - Ganguly, Sanmay

AU - Gross, Eilam

AU - Heinrich, Lukas

AU - Kado, Marumi

AU - Kakati, Nilotpal

AU - Rieck, Patrick

AU - Santi, Lorenzo

AU - Tusoni, Matteo

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Configurable calorimeter simulation for AI applications

Abstract

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